October 2000
Intermediate to advanced
288 pages
9h 22m
English
book
Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
by Albert Y. Zomaya, Fikret Ercal, Stephan Olariu
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8.2 THE TASK-MAPPING PROBLEM
A set of parallel communicating tasks can be modeled by a node- and edge-weighted graph 
. A node set Vp represents the tasks (e.g., some program modules), and an edge set Ep represents the communication pattern between these tasks. The weight associated with a node represents the amount of time needed for a processor to execute the task. The weight associated with an edge indicates the total amount of communication required, in time or volume of data exchange, between two individual tasks. Many applications can be modeled as task-interaction graphs. Examples of these applications are parallel numerical algorithms such as parallel Gaussian elimination, parallel Laplace equation solver, and parallel LU decomposition.
The application to be solved can be represented by an undirected or a directed graph. A directed graph corresponds to a task-precedence graph where tasks have data dependencies among them. The problem in this case is to find an optimal schedule without violating precedence constraints among the tasks. In other words, decisions have to be made to assign tasks to processors by ordering task execution on each node and ordering intermediate data transfers [16, 29], Task scheduling is covered in another chapter of this book.
In contrast to applications represented by task-precedence graphs, execution dependencies are not enforced between tasks in ...
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